In [2]:
import pandas as pd
import matplotlib.pyplot as plt
from IPython.core.display import display, HTML
import gmaps
import seaborn as sns
low_memory=False
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 50110)
pd.set_option('display.width', 1000111)
pd.set_option('display.max_colwidth', 1000111)
1. Introduction¶
Ford Gobike is a regional bike sharing public service in the Bay Area, CA, USA. In 2017, the service brought Ford Motor Company on-board to expand the scale of their operations. Currently, Ford Gobike serves the Bay Area, East Bay and San Jose. (Etherington, Darrell (June 27, 2017). "Ford GoBike launches in the Bay Area starting tomorrow". TechCrunch. Retrieved July 23, 2017.)
They have released their bike share data in the CSV format on their website. We will use their publicly available dataset for this project. This project aims to study the effects of weather on the quality and number of bike rides that took place in the Bay Area in the year of 2018.
Given our datasets, we want to describe them in detail to understand the patterns of weather and biking. Analyzing them will give us insights on how the data are structured and whether there have been any outliers due to events in the area. For instance, an unusually high number of bike rides might be observed due to a concert or a public event in that area.
Contrastingly, the recent California wildfires would have reduced the number of bikers.
Furthermore, The ability to predict the number of rides due to weather can allow the businesses that oversee demand and planning systems to manage them in a more efficient and cost-effective manner.
While doing the above, we will also attempt to understand the characteristics of our user base. We have a limited number of variables describing them, but we will equate the social and economic aspects of our user base to understand their biking patterns.
2. Data Description- Biking Dataset¶
In [3]:
filepath = "C:/Users/pujar/Dropbox/Semester 2/Python-BUDT758X/Pawject/data/"
desc = pd.read_csv(filepath+"desc_bike.csv",encoding='utf-8')
desc
Out[3]:
3. Data Description- Weather Dataset¶
In [4]:
desc = pd.read_csv(filepath+"desc_weather.csv",encoding='utf-8')
desc
Out[4]:
4. Datasets loaded, cleaned, and transformed¶
4.1 Separate start_time and end_time to get dates and times¶
In [6]:
wdf = pd.read_csv(filepath+"SF_Weather_2018.csv",encoding='utf-8')
bdf = pd.read_csv(filepath+'all.csv', encoding='utf-8', sep=',')
#Creating more columns to process the timestamp to date and time separately
bdf.insert(2, 'start_date', 0)
bdf.insert(3, 'end_date', 0)
bdf.insert(4, 'start_timing', 0)
bdf.insert(5, 'end_timing', 0)
# Converting timestamp to string type for easy REGEX-ing (if that's a term!)
bdf['start_time'] = bdf['start_time'].astype(str)
bdf['end_time'] = bdf['end_time'].astype(str)
bdf['start_date'] = bdf['start_time'].str.extract('(\d\d\d\d-\d\d-\d\d)')
bdf['start_timing'] = bdf['start_time'].str.extract('(\d\d:\d\d:\d\d.\d\d\d\d)')
bdf['end_date'] = bdf['end_time'].str.extract('(\d\d\d\d-\d\d-\d\d)')
bdf['end_timing'] = bdf['end_time'].str.extract('(\d\d:\d\d:\d\d.\d\d\d\d)')
4.2 Remove additional headers that Pandas duplicated¶
In [7]:
# NOTE- Pandas seems to re-add CSV headers every 100,000 or so rows.
# Therefore, we have removed the rows with repeating headers.
bdf = bdf[bdf['start_time'] != 'start_time']
4.3 Merge the two datasets to equate weather and biking data¶
In [8]:
# Merge the two dataframes to get weather data corresponding to bike rides
wdf['Date'] = pd.to_datetime(wdf['Date']).dt.strftime('%Y-%m-%d')
df = pd.merge(bdf, wdf, left_on='start_date', right_on='Date')
4.4 Remove columns that are not relevant¶
In [9]:
del wdf['New Snow']
del wdf['Snow Depth']
wdf.head()
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5. Breakdown of Rides per month¶
According to the below bar chart, it is evident that summer months tend to have higher biking rates. A sharp decline in biking is observed from Oct-Nov (shaded in graph). Further research shows that the Californian wildfires reduced air quality manifold. Due to high levels of pollution, San Franciscans preferred to avoid open air exertion.¶
Source: https://www.popsci.com/fires-california-air-quality-cigarettes
In [10]:
from itertools import cycle, islice
# Using different colors to represent data
mycolors= "#6C5B7B"
# Extract the month using REGEX
ser = df['start_date'].str.extract("(-\d{1,2})")
ser[0] = ser[0].str.replace('-','')
ser = ser[0].groupby(ser[0]).count()
# Plot
plt.figure(figsize=(19,7), facecolor='white')
plt.title("Popular biking months")
plt.ylabel('Frequency of rides')
plt.xlabel('Month')
ser.plot.bar(x='lab', y='val', rot=0, color=mycolors)
plt.axvspan(8.5, 10.5, color='black', alpha=0.5)
plt.show()
6. Daily usage of bike rides & weather patterns¶
San Francisco's weather is strongly influenced by the cool currents of the Pacific Ocean on the west side of the city. This moderates temperature swings and produces a remarkably mild year-round climate with little seasonal temperature variation.¶
As we can see from the plot, the temperature in SF is between 40 degrees to 80 degrees the whole year which is quite comfortable for riding bike outdoors¶
Also, cooler months have fewer rides than warmer months.¶
In [11]:
#Create a new dataset that has count the date as the number of rides
df1=df.Date.value_counts()
df1=df1.to_frame()
df1=df1.rename(columns={'Date':'Number of Bike rides'})
df1.index.names = ['Date']
#sort index as date
df1=df1.sort_index()
from datetime import datetime
import matplotlib.dates as mdate
import matplotlib as mpl
import datetime as dt
import matplotlib.pyplot as py
#Generate two plot--weather pattern and daily usage of bike rides
fig = plt.figure(figsize=(18,12),facecolor='white')
ax1 = fig.add_subplot(2,1,1) # Add the first plot on the top
df1.index = pd.to_datetime(df1.index, format='%Y-%m-%d')#Transform the data type and used for changing the x axis later
plt.title('Daily usage of bike rides - 2018, SF',fontsize=16)
plt.xlabel('Date',fontsize=12)
#Change the x axis as months
ax1.xaxis.set_major_formatter(mdate.DateFormatter('%b %Y'))
dateFmt = mpl.dates.DateFormatter('%Y-%m')
ax1.xaxis.set_major_formatter(dateFmt)
#Give the range of x axis from 2018-1-1 to 2018-12-31 and set the frequncy of x label as months
plt.xticks(pd.date_range('2018-01','2018-12',freq='MS'),rotation=30)
plt.ylabel("Number of bike rides",fontsize=12)
plt.ylim([0,8500])
plt.plot(df1.index,df1['Number of Bike rides'],label='Number of bike rides',c='blue',alpha=0.3)
#Add the second plot on the bottom
fig = plt.figure(figsize=(18,12), facecolor='white')
ax = fig.add_subplot(2,1,2)
wdf['Date'] = pd.to_datetime(wdf['Date'], format='%Y-%m-%d')
plt.plot(wdf['Date'],wdf['Maximum'],label='Maximum temperature',c='#F8B195',alpha=0.5)
plt.plot(wdf['Date'],wdf['Minimum'],label='Minimum temperature',c='#C06C84',alpha=0.5)
plt.title('Daily high and low temperatures - 2018, SF',fontsize=16)
plt.xlabel('Date',fontsize=16)
#Change the x axis again to months
plt.gca().xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d'))
plt.gca().xaxis.set_major_locator(mdate.MonthLocator())
#Give the range of x axis from 2018-1-1 to 2018-12-31 and set the frequncy of x label as months
plt.xticks(pd.date_range('2018-01-01','2018-12-31',freq="MS"),rotation=30)
plt.ylabel("Temperature ( F )",fontsize=12)
plt.ylim([20,100])
plt.tick_params(axis='both',which='major',labelsize=12)
plt.legend()
plt.show()
7. Most popular biking hours based on Start Time¶
This resembles a bimodal distribution.
Biking peaks at the start of office hours (from 6-10 AM) and does so again after the end of office hours (4-7 PM).
In [12]:
# Extract the hour of the day using REGEX
ser = df['start_timing'].astype('str')
ser = ser.str.extract("(\d{1,2})")
ser = ser[0].groupby(ser[0]).count()
# Plot
plt.figure(figsize=(19,7), facecolor='white')
plt.title("Popular biking hours")
plt.ylabel('Frequency of rides')
plt.xlabel('Hour of the day')
sns.pointplot(x=ser.index, y=ser.values,color='red');
plt.show()
8. Most popular locations of trips¶
In [13]:
print(df['start_station_name'].value_counts().sort_values(ascending=False).head(10))
According to the below map, most trips start around popular transport hubs and offices¶
In [12]:
import gmaps
import gmaps.datasets
key = "AIzaSyAuS0BiPZ4uvjhE3ZHLpO7_ZFzVlW_THJ4"
# Use google maps api
gmaps.configure(api_key=key) # Fill in with your API key
#set up locations
start_locations = df[['start_station_latitude', 'start_station_longitude']]
start_df=start_locations.sample(400000)
start_df1= start_df.groupby(by=['start_station_latitude','start_station_longitude'])
start_df2= pd.DataFrame(start_df1.size())
start_df2.rename(columns={0: 'Numbers'}, inplace=True)
start_df2=start_df2.reset_index()
start_df2['Numbers']=start_df2['Numbers'].astype('float')
start_df2['start_station_latitude']=start_df2['start_station_latitude'].astype('float')
start_df2['start_station_longitude']=start_df2['start_station_longitude'].astype('float')
#set up map
start_fig = gmaps.figure()
heatmap_layer = gmaps.heatmap_layer(
start_df2[['start_station_latitude','start_station_longitude']], weights=start_df2['Numbers'],
max_intensity=100, point_radius=5.0
)
start_fig.add_layer(heatmap_layer)
start_fig
In [13]:
print(df['end_station_name'].value_counts().sort_values(ascending=False).head(10))
In [14]:
#set up locations
end_locations = df[['end_station_latitude','end_station_longitude']]
end_df=end_locations.sample(400000)
end_df1= end_df.groupby(by=['end_station_latitude','end_station_longitude'])
end_df2= pd.DataFrame(end_df1.size())
end_df2.rename(columns={0: 'Numbers'}, inplace=True)
end_df2=end_df2.reset_index()
end_df2['Numbers']=end_df2['Numbers'].astype('float')
end_df2['end_station_latitude']=end_df2['end_station_latitude'].astype('float')
end_df2['end_station_longitude']=end_df2['end_station_longitude'].astype('float')
#set up map
end_fig = gmaps.figure()
heatmap_layer = gmaps.heatmap_layer(
end_df2[['end_station_latitude','end_station_longitude']], weights=end_df2['Numbers'],
max_intensity=100, point_radius=5.0
)
end_fig.add_layer(heatmap_layer)
end_fig
9. Breakdown of Clientele Gender¶
Majority of the clientele is Male, followed by Female¶
In [14]:
# Replace garbage values with something meaningful- Some more cleaning done
df['member_gender'] = df['member_gender'].str.replace('member_gender','Unknown')
df_gender = df['member_gender'].dropna()
# Get categories and their frequencies
df_gender = df_gender.value_counts().sort_values(ascending=False)
plt.figure(figsize=(19,7), facecolor='white')
plt.title("Gender Overview")
plt.ylabel('Number')
plt.xlabel('Gender')
ax = df_gender.plot.bar(x='lab', y='val', rot=0, color=mycolors)
plt.show()
10. Age Breakdown¶
Most bike riders in this ecosystem seem to be young working professionals as seen by the below age breakdown and above location charts. (Median age = 34).¶
The barplot shows that the distribution of age is right-skewed indicating a young population.¶
There are a few outliers according to the boxplot. Some of the senior San Franciscans have driven Ford GoBikes. However, there is some invalid data maybe due to human negligence as exhibited by age=134 in the boxplot.¶
In [15]:
import numpy as np
import datetime
# Calculate member ages- certain ages are NaN and hence dropped
now = datetime.datetime.now()
df_clean_age = df[df['member_birth_year'] != 'member_birth_year']['member_birth_year']
df_clean_age = df_clean_age.dropna()
ser_age = float(now.year) - df_clean_age.astype('float64')
ser_age_box = ser_age.astype('float64')[ser_age.index<100]
# Create bins for a histogram
# bins = np.linspace(start=ser_age.min(), stop=ser_age.max(), num=13)
# ser_age = pd.cut(ser_age, bins)
ser_age_bar = ser_age.value_counts().sort_index(ascending=True)
ser_age_bar = ser_age_bar[ser_age_bar.index<100].to_frame().reset_index()
# Generate plot
fig, ax = plt.subplots(2, 1, sharex=False, figsize=(19,7), facecolor='white')
plt.title("Frequency of Age")
plt.ylabel('Number of People')
plt.xlabel('Age')
ser_age_box.plot.box(vert=False, ax=ax[1])
ser_age_bar.plot.bar(y='member_birth_year', x='index', rot=0, color=mycolors, ax=ax[0], legend=False)
plt.show()
11. Quantifying the Impact of the Weather on Citi Bike Activity¶
From the plot, we can see that when the precipitation value is larger than 1.0, it rarely has bike trips longer than 20,000 seconds or 5 hours. It shows that the heavier the rain is, the shorter is the trip duration.¶
Some of the trips have duration spanning more than 1 day. This could happen due to in-app errors or users simply forgetting to finish the trips in their apps thereby leading to a longer duration. Bike rides spanning more than 1 day do not sound feasible.¶
In [16]:
#Transform the types of "precipitation" and "duration_sec" to float
df['Precipitation'] = df['Precipitation'].astype('float64')
df['duration_sec'] = df['duration_sec'].astype('float64')
#Extract the dataframe which contains the rainy data
df_rainy = df[df['Precipitation']>0]
#Plot the Rain vs duration of bike rides
plt.figure(figsize=(15,5), facecolor='white')
plt.plot(df_rainy['Precipitation'],df_rainy['duration_sec'],'g.')
plt.xlabel('Precipitation')
plt.ylabel('Duration (seconds)')
plt.show()
12. Rain vs number of bike rides¶
From the plot, we can see the number of bike rides decreases as the precipitation value gets larger. As a result, we have the evidence to conclude that the rainy weather has a negative effect on the number of bike rides.¶
In [17]:
#Rain vs number of bike rides
num_ride_rainy=df_rainy['Precipitation'].value_counts()
num_ride_rainy
df2 = num_ride_rainy.to_frame()
column = df2.columns[0]
df2['index'] = df2.index.tolist()
plt.figure(figsize=(15,5), facecolor='white')
plt.plot(df2['index'],df2['Precipitation'],'g.')
plt.xlabel('Precipitation')
plt.ylabel('Numbers of bike rides')
plt.show()
In [18]:
#Using group by function to generate two coloums of maximum temperature and start_date under same maximum teperature
#Group by again and calculate the mean usage of the different dates under same maximum temperature
df3=df[['start_date','Maximum']].groupby(["Maximum", "start_date"]).size().reset_index(name="usage").groupby(by='Maximum')['usage'].agg('mean').sort_values(ascending=False)
df3.sort_index().head()
Out[18]:
13. Maximum teperature VS average daily usage of bike rides¶
This graph makes shows there is a nonlinear relationship between rides and max daily temperature. The number of trips ramps up quickly between 55 and 65 degrees and above 80 degrees, but between 65 degrees and 80 degrees there’s a much weaker relationship between ridership and temperature. In general, it’s not surprising that the warmer the temperature is, the higher the number of average number of bike rides.¶
In [19]:
#Generate the plot of Maximum teperature VS Average daily usage of bike rides
fig = plt.figure(figsize=(10,6), facecolor='white')
plt.plot(df3,'y.',label='Average numbers of bike rides')
plt.title('Maximum teperature VS Average daily usage of bike ride')
plt.xlabel('Maximum temperature',fontsize=12)
plt.ylabel('Average numbers of bike rides',fontsize=12)
plt.legend()
plt.show()
14. Low income bike plan (bike_share_for_all_trip)¶
14.1 Gender vs bike_share_for_all_trip¶
**bike_share_for_all_trip**- This payment plan is for low income customers who want cheaper rides (they must qualify to get this!)¶
Although the number of male users is much higher than that of females, the proportion of males availing the low income plan is almost the same as that of females.¶
In [20]:
fig = plt.figure(figsize=(10,6), facecolor='white')
httable = pd.crosstab(index=df.member_gender, columns=df.bike_share_for_all_trip,normalize='index').loc[['Male','Female','Other']]
sns.heatmap(httable, cmap='YlGnBu', annot=True, square=False, linewidths=1)
plt.xlabel('');
14.2 User type (Subscriber vs Customer) vs bike_share_for_all_trip¶
In the plot below, we can see that more subscribers avail the low income payment plan than casual customers. The latter do not avail the plan since they use bikes relatively infrequently and are prepared to pay full price per ride¶
In [51]:
fig = plt.figure(figsize=(10,6), facecolor='white')
httable = pd.crosstab(index=df.user_type, columns=df.bike_share_for_all_trip).loc[['Subscriber','Customer']]
sns.heatmap(httable, cmap='Oranges', annot=True, fmt='d', square=False, linewidths=1)
plt.xlabel('');
15. User type; gender distribution¶
90% of males are subscribers, and 85% of females are subscribers. A higher proportion of women are casual bikers.¶
In [52]:
fig = plt.figure(figsize=(10,6), facecolor='white')
httable = pd.crosstab(index=df.member_gender, columns=df.user_type,normalize='index').loc[['Male','Female','Other']]
sns.heatmap(httable, cmap='Purples', annot=True, square=False, linewidths=1)
Out[52]:
16. Prediction- Weather's effect on number of bike rides¶
16.1 Function to draw Predicted vs Actual values¶
In [26]:
def draw_pred_plot(y):
fig = plt.figure(figsize=(12,8), facecolor='white')
plt.plot(y_test, color = 'red', label = 'Real data')
plt.plot(y, color = 'blue', label = 'Predicted data')
plt.title('Prediction')
plt.legend()
plt.show()
16.2 Intermediate data structure¶
In [22]:
df0 = df[['Average', 'Departure', 'HDD', 'CDD','Precipitation','start_date']]
df0=df0.groupby('start_date').mean()
df1=df[['Average', 'Departure', 'HDD', 'CDD','Precipitation','start_date']]
df1 = df1.groupby('start_date').count()['Average']
df0['count']=df1
df0.head()
Out[22]:
16.3 Split data into training and test¶
In [23]:
from sklearn.model_selection import train_test_split, KFold, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor, export_graphviz
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import confusion_matrix
from sklearn.metrics import mean_squared_error
np.random.seed(12345)
dfr = df0[['Average', 'Departure', 'HDD', 'CDD','Precipitation']]
X_train, X_test, y_train, y_test = train_test_split(dfr, df0['count'].values, test_size=0.2)
16.4 Linear Regression¶
According to the below coefficients, all else being constant, the number of bike rides increases by 332 when the average temperature increases by 1F. Similarly, when there is a unit increase in precipitation, all else remaining equal, the number of bike rides decreases by 1543.¶
In [24]:
lr = LinearRegression(fit_intercept=True)
lr.fit(X_train, y_train)
print('The out-of-sample R2 is', lr.score(X_test, y_test))
pd.DataFrame({'Feature': dfr.columns, 'Coefficient': lr.coef_}, columns=['Feature','Coefficient'])
Out[24]:
16.5 Prediction using Linear Regression¶
In [27]:
y = lr.predict(X_test)
draw_pred_plot(y)
r2lr = lr.score(X_test, y_test)
mselr = mean_squared_error(y, y_test)
print("R^2: ", r2lr)
print("MSE Linear Regression: ", mselr)
17. Regression Tree¶
In [28]:
from sklearn import tree
# Fit the initial tree
tr = tree.DecisionTreeRegressor()
tr.fit(X_train, y_train)
r2tr = tr.score(X_test, y_test)
print('The out-of-sample R2 is', r2tr)
params = {'min_samples_split': [5,10,25,50], 'max_depth': [5,10,25]}
tree_grid = GridSearchCV(estimator=tr, param_grid=params, scoring='r2', cv=5, n_jobs=4)
tree_grid.fit(X_train, y_train)
# Assign best estimator to variable and evaluate performance
tree = tree_grid.best_estimator_
y = tree.predict(X_test)
draw_pred_plot(y)
r2tr = tree.score(X_test, y_test)
msetr = mean_squared_error(y, y_test)
print("Tree R^2: ",r2tr)
print("MSE Tree: ", msetr)
17.2 Visualize the tree to see rules¶
Each leaf node represents the number of trips this model would predict. Each decision node represents a business rule. Even though the predictive power of this model is not as good as Linear Regression, we can use the below image to visualize some of the rules for a better understanding of the biking phenomenon at a general level.¶
In [30]:
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
dot_data = StringIO()
export_graphviz(tree, out_file=dot_data, filled=True, rounded=True,special_characters=True, feature_names = X_train.columns)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png('Biking.png')
Image(graph.create_png())
Out[30]:
18 Ensemble Methods- Boosting¶
NOTE: Boost the Tree above¶
18.1 Set up boosting with tree¶
In [31]:
from sklearn import ensemble as en
from sklearn import metrics
dt5 = en.AdaBoostRegressor(random_state=1, base_estimator=tree)
regr =dt5.fit(X_train, y_train)
fig=plt.figure()
y = tr.predict(X_test)
draw_pred_plot(y)
r2b = regr.score(X_test, y_test)
mseb = mean_squared_error(y, y_test)
print("Boosting R^2: ", r2b)
y = regr.predict(X_test)
print("MSE Boosting:", mseb)
18.2 Most important variables¶
According to the boosted tree, Average temperature, Departure and Precipitation are the most important predictors in predicting number of bike rides.¶
In [32]:
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
import plotly.plotly as py
import plotly.figure_factory as ff
from plotly.graph_objs import *
import plotly.graph_objs as go
init_notebook_mode()
data = [go.Bar(
x=X_test.columns.tolist(),
y=regr.feature_importances_.tolist()
)]
iplot(data)
19 Neural Networks¶
In [37]:
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
19.1 Feature Scaling¶
In [38]:
#Applying Min-Max Feature scaling
from sklearn.preprocessing import MinMaxScaler
min_max_sc = MinMaxScaler()
X_train1 = min_max_sc.fit_transform(X_train)
X_test1 = min_max_sc.transform(X_test)
y_train1=(y_train-y_train.min())/(y_train.max()-y_train.min())
19.2 Set up neural networks¶
In [39]:
np.random.seed(12345)
nn = Sequential()
nn.add(Dense(units = 4, kernel_initializer = 'uniform', activation = 'relu', input_dim = dfr.shape[1]))
nn.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'relu'))
nn.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])
nn.fit(X_train1, y_train1, batch_size = 10, epochs = 100)
Out[39]:
19.3 Export neural networks diagram¶
In [42]:
from ann_visualizer.visualize import ann_viz
ann_viz(nn, view=True, filename="neuralnetwork", title="Neural Network")
19.4 Prediction using neural networks¶
In [43]:
y_pred = nn.predict(X_test1)
y_pred1= y_pred*(y_train.max()-y_train.min())+y_train.min()
fig=plt.figure()
fig = plt.figure(figsize=(12,8), facecolor='white')
plt.plot(y_test, color = 'red', label = 'Real data')
plt.plot(y_pred1, color = 'blue', label = 'Predicted data')
plt.title('Prediction')
plt.legend()
plt.show()
msenn = mean_squared_error(y_pred1, y_test)
print("MSE Neural Network:",msenn)
20 Summarizing the predictive models¶
20.1 Compare their R^2 Values¶
Linear regression has the highest R^2 value¶
In [44]:
r2json = {"Linear Regression":r2lr, "Decision Trees":r2tr, "Boosting":r2b}
fig = plt.figure(figsize=(12,8), facecolor='white')
plt.bar(*zip(*r2json.items()))
plt.xlabel('----Method----')
plt.ylabel('R^2')
plt.show()
20.2 Compare their MSE values¶
Linear Regression has the lowest MSE value¶
In [45]:
msejson = {"Linear Regression":mselr, "Decision Trees":msetr, "Boosting":mseb, "Neural Network":msenn}
fig = plt.figure(figsize=(12,8), facecolor='white')
plt.bar(*zip(*msejson.items()))
plt.xlabel('----Method----')
plt.ylabel('MSE')
plt.show()
21. Conclusion¶
Owing to the above results, it is clear that linear regression captures the relationship between independent and dependent variables best (out of the methods we compared). This leads us to believe that the relationship is more linear than non-linear.
The above predictive and explanatory models should help the management at Ford GoBikes plan their resources more effectively. Weather reports are typically released 7 days prior, and that should enable planners at the company to check where demand is highest.
Biking is generally more environmentally friendly as compared to using fossil-fuel-powered vehicles, and would be a blessing to ease traffic congestion in the city (as shown in the Google maps plug in above). Also, it is a good way to relieve stress for both adults and children.
To cater to more people, Ford GoBike could expand their network further in areas such as West San Francisco where there are not many trips originating/ending. The presence of parks such as The Golden Gate Park and Sunset Reservoir park in West San Francisco would be good locations to deploy bikes.
Furthermore, they can create incentive programs for seniors, children and women to take up more biking as is evident from skewed demographic profiles observed above. As they plan on expanding further, they will find our predictive models useful in determining how many rides to expect.